2 citations · 3 across the 4 of their papers we have counts for
4 papers
HyperTTS: Parameter Efficient Adaptation in Text to Speech using Hypernetworks
Yingting Li, Rishabh Bhardwaj, Ambuj Mehrish +2
Neural speech synthesis, or text-to-speech (TTS), aims to transform a signal from the text domain to the speech domain. While developing TTS architectures that train and test on th…
CM-TTS: Enhancing Real Time Text-to-Speech Synthesis Efficiency through Weighted Samplers and Consistency Models
Xiang Li, Fan Bu, Ambuj Mehrish +4
Neural Text-to-Speech (TTS) systems find broad applications in voice assistants, e-learning, and audiobook creation. The pursuit of modern models, like Diffusion Models (DMs), hold…
ADAPTERMIX: Exploring the Efficacy of Mixture of Adapters for Low-Resource TTS Adaptation
Ambuj Mehrish, Abhinav Ramesh Kashyap, Li Yingting +2
There are significant challenges for speaker adaptation in text-to-speech for languages that are not widely spoken or for speakers with accents or dialects that are not well-repres…
Evaluating Parameter-Efficient Transfer Learning Approaches on SURE Benchmark for Speech Understanding
Yingting Li, Ambuj Mehrish, Shuai Zhao +5
Fine-tuning is widely used as the default algorithm for transfer learning from pre-trained models. Parameter inefficiency can however arise when, during transfer learning, all the…